“Some Account of an Extraordinary Traveller”: Using Virtual Tours to Access Remote Heritage Sites of Inuit Cultural Knowledge
Bibliographic record
Abstract
The use of panoramic images to transport viewers to remote geographic locations can be traced back to the panorama theatres of nineteenth-century Victorian London. More recently, Google’s World Wonders Project has utilized 360-degree panospheres to capture some of the world’s most famous heritage sites. Using arrows that demarcate a defined path of movement, users can virtually tour these sites by “jumping” from one panosphere to the next. Arvia’juaq National Historic site is located near the community of Arviat. Although the heritage value of the site is highly significant, Arvia’juaq sees few national and international visitors because of its remote location. For a variety of reasons, some local Inuit also find it difficult to regularly visit the site even though it is an important source of cultural identity. In this paper, we explore how panospheres can be used to create interactive virtual tours of heritage sites like Arvia’juaq. Although there are some caveats, we argue that virtual reality (VR) tours are potentially powerful tools for connecting people to heritage sites that might otherwise be inaccessible. This has important implications for raising awareness of polar heritage and its significance to Indigenous people, as well as national and international audiences.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.012 | 0.007 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".